Best practices in scleroderma: an analysis of practice variability in SSc centres within the Canadian Scleroderma Research Group (CSRG).
Bibliographic record
Abstract
OBJECTIVES: There is currently no consensus on best practice in systemic sclerosis (SSc). To determine if variability in treatment and investigations exists, practices among Canadian Sclerodermia Research Group (CSRG) centres were compared. METHODS: Prospective clinical and demographic data from adult SSc patients are collected annually from 15 CSRG treatment centres. Laboratory parameters, self-reported socio-demographic questionnaires, current and past medications and disease outcome measures are recorded. For centres with >50 patients enrolled, treatment practices were analysed to determine practice variability. RESULTS: Data from 640 of 938 patients within the CSRG database met inclusion criteria, where 87.3% were female, the mean ± SEM age was 55.3±0.5, 48.9% had limited SSc and 47.8% had diffuse SSc (and 3.3% uncharacterised). Some investigation and treatment practices were inconsistent among 6 centres including proportion receiving: PDE5 (phosphodiesterase type 5) inhibitors for Raynaud's phenomenon (p=0.036); cyclophosphamide (p=0.037) and azathioprine (p=0.037) for treatment of ILD; and current use of D-penicillamine, although uncommon, varied among sites. Annual echocardiograms and PFTs were frequently done and did not vary among sites but the rate of pulmonary arterial hypertension (PAH) was directly related to site size and this was not the case for other organ involvement. CONCLUSIONS: Despite routine tests within a database, site variation in SSc with respect to investigations and management among CSRG centres exists suggesting a need for a standardised approach to the investigation and treatment of SSc. One can speculate that larger centres are more export in detecting PAH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".